A Critical Pedagogy-Based Andragogical Self-Learning Framework for AI/IoT-Enabled Hybrid Adult Education: A Systematic Review and Conceptual Model Development

Amarulloh (Department of Education of Doctoral, FKIP University of Lampung, Lampung, Indonesia)
Herpratiwi (Department of Education of Doctoral, FKIP University of Lampung, Lampung, Indonesia)
Rangga Firdaus (Department of Education of Doctoral, FKIP University of Lampung, Lampung, Indonesia)
Viyanti (Department of Education of Doctoral, FKIP University of Lampung, Lampung, Indonesia)

JTP - Jurnal Teknologi Pendidikan
Vol. 28 No. 1 (2026): Jurnal Teknologi Pendidikan

This study aims to develop a validated conceptual model—the Andragogical Self-Learning Framework for AI/IoT-Enabled Hybrid Education (ASFAIHE) that integrates Knowles’ andragogy and heutagogy with AI/IoT-enabled hybrid learning environments, mediated by critical pedagogy principles of learner agency and digital equity. Method: A systematic mixed-methods review was conducted following PRISMA 2020 guidelines. Electronic searches were performed across five major databases (PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar) for peer-reviewed studies published between 2005 and 2025. Inclusion criteria required studies to address adult learning theories (specifically andragogy or related models), AI and/or IoT in educational contexts, critical pedagogy principles, and hybrid or digital learning environments. Of 3,456 initially identified records, 85 studies met all inclusion criteria after two-stage independent screening. Qualitative data were analyzed through thematic synthesis following Thomas and Harden’s approach using NVivo; quantitative findings were synthesized via random-effects meta-analysis using R’s ‘metafor’ package; bibliometric mapping was conducted using VOSviewer. Results: Thematic synthesis yielded four interrelated themes: (1) Learner Autonomy and Scaffolding, (2) Adaptive Feedback Loops, (3) Contextual Sensing via IoT, and (4) Data Privacy and Ethical Concerns. Meta-analysis revealed that AI-driven adaptive systems significantly enhance learner engagement (pooled g = 0.65; 95% CI [0.52, 0.78], p < 0.001) and self-efficacy (g = 0.58; 95% CI [0.45, 0.71], p < 0.001). These findings were integrated into the ASFAIHE model, which conceptualizes adult learner engagement as a function of AI personalization, IoT contextual feedback, and critical consciousness. Contribution: This study produces a theoretically grounded and empirically supported conceptual model that constitutes a novel design architecture for hybrid AI learning ecosystems in adult education. The model advances existing frameworks by systematically embedding critical pedagogy as an ethical and transformative mediator. Longitudinal and cross-cultural empirical validation is recommended to strengthen the model’s generalizability.